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Frequently Asked Questions

This document provides answers to some of the frequently asked questions about TensorFlow.

Features and Compatibility

Can I run distributed training on multiple GPUs?

Yes! TensorFlow gained support for distributed computation back in version 0.8, see the distributed computation guide. TensorFlow supports multiple devices (CPUs and GPUs) on one or more computers.

Does TensorFlow work with Python 3?


TensorFlow graphs and eager execution

See the graphs and sessions guide and the eager execution guide.

Do TensorFlow operations return immediately?

If you enable eager execution, operations like c = tf.matmul(a, b) are executed immediately. See the eager execution guide for using eager execution to create more readable, intuitive TensorFlow code.

However, without eager execution enabled, an operation like tf.matmul above does not execute immediately, but, instead, builds a fragment of a TensorFlow graph.

Why graphs? TensorFlow graphs can help with distribution, optimization, and putting models into production. In the suggested expression, a, b, and c are tf.Tensor objects. A tf.Tensor object is a symbolic handle to the result of an operation, but does not actually hold the values of the operation's output. Instead, you can build up complicated expressions (such as entire neural networks and their gradients) as a dataflow graph. You then offload the computation of the entire dataflow graph (or a subgraph of it) to a TensorFlow tf.Session, which is able to execute the whole computation much more efficiently than executing the operations one-by-one.

Note: In upcoming TensorFlow 2.0, all operations will be eagerly executed.

How are devices named?

The supported device names are "/device:CPU:0" (or "/cpu:0") for the CPU device, and "/device:GPU:i" (or "/gpu:i") for the ith GPU device.

How do I place operations on a particular device?

To explicitly place a group of operations on a device, create them within a tf.device context. See the using GPUs guide for details about how TensorFlow assigns operations to devices.

You can also look at CIFAR-10 tutorial for an example model that uses multiple GPUs.

As of r1.12, we recommend trying tf.contrib.distribute.DistributionStrategy as an easy way to distribute computation with Keras and Estimator models. It is under development.

Running a TensorFlow computation

What is feeding and placeholders?

The recommended way of providing data to a model for training or inference is via the API; see the Importing Data guide.

However, in some older models you may find feeds and placeholders. Feeding is a mechanism in the tf.Session API that allows you to substitute different values for one or more tensors at run time. The feed_dict argument to is a dictionary that maps tf.Tensor objects to numpy arrays (and some other types), which will be used as the values of those tensors in the execution of a step.

What is the difference between and Tensor.eval()?

If t is a tf.Tensor object, tf.Tensor.eval is shorthand for, where sess is the current tf.get_default_session. The two following snippets of code are equivalent:

# Using ``.
sess = tf.Session()
c = tf.constant(5.0)

# Using `Tensor.eval()`.
c = tf.constant(5.0)
with tf.Session():

In the second example, the session acts as a context manager, which has the effect of installing it as the default session for the lifetime of the with block. The context manager approach can lead to more concise code for simple use cases (like unit tests); if your code deals with multiple graphs and sessions, it may be more straightforward to make explicit calls to

Do Sessions have a lifetime? What about intermediate tensors?

Sessions can own resources, such as tf.Variable, tf.QueueBase, and tf.ReaderBase. These resources can sometimes use a significant amount of memory, and can be released when the session is closed by calling tf.Session.close.

Does the runtime parallelize parts of graph execution?

When you use graph execution, the TensorFlow runtime parallelizes execution across many different dimensions:

  • The individual ops have parallel implementations, using multiple cores in a CPU, or multiple threads in a GPU.
  • Independent nodes in a TensorFlow graph can run in parallel on multiple devices, which makes it possible to speed up CIFAR-10 training using multiple GPUs.
  • The Session API allows multiple concurrent steps (i.e. calls to in parallel). This enables the runtime to get higher throughput, if a single step does not use all of the resources in your computer.

Why does hang when using a reader or a queue?

Note: Queues are discouraged in favor of, which provides a simpler interface and improved performance.

The tf.ReaderBase and tf.QueueBase classes provide special operations that can block until input (or free space in a bounded queue) becomes available. These operations allow you to build sophisticated input pipelines, at the cost of making the TensorFlow computation somewhat more complicated. See the how-to documentation for using QueueRunner objects to drive queues and readers for more information on how to use them.


See the variables guide.

Should I turn on use_resource=True when constructing variables?

Yes. This uses safer memory behavior, and will be the default in TensorFlow 2.0.

What is the lifetime of a variable?

A variable is created when you first run the tf.Variable.initializer operation for that variable in a session. It is destroyed when that tf.Session.close.

In eager execution, variables are freed when their associated Python objects are cleaned up.

How do variables behave when they are concurrently accessed?

Variables allow concurrent read and write operations. The value read from a variable may change if it is concurrently updated. By default, concurrent assignment operations to a variable are allowed to run with no mutual exclusion. To acquire a lock when assigning to a variable, pass use_locking=True to tf.Variable.assign.

Tensor shapes

See also the tf.TensorShape.

How can I determine the shape of a tensor in Python?

In TensorFlow, a tensor has both a static (inferred) shape and a dynamic (true) shape. The static shape can be read using the tf.Tensor.get_shape method: this shape is inferred from the operations that were used to create the tensor, and may be partially complete (the static-shape may contain None). If the static shape is not fully defined, the dynamic shape of a tf.Tensor, t can be determined using tf.shape(t).

What is the difference between x.set_shape() and x = tf.reshape(x)?

The tf.Tensor.set_shape method updates the static shape of a Tensor object, and it is typically used to provide additional shape information when this cannot be inferred directly. It does not change the dynamic shape of the tensor.

The tf.reshape operation creates a new tensor with a different dynamic shape.

How do I build a graph that works with variable batch sizes?

It is often useful to build a graph that works with variable batch sizes so that the same code can be used for (mini-)batch training, and single-instance inference. The resulting graph can be tf.Graph.as_graph_def and tf.import_graph_def.

When building a variable-size graph, the most important thing to remember is not to encode the batch size as a Python constant, but instead to use a symbolic Tensor to represent it. The following tips may be useful:

  • Use batch_size = tf.shape(input)[0] to extract the batch dimension from a Tensor called input, and store it in a Tensor called batch_size.

  • Use tf.reduce_mean instead of tf.reduce_sum(...) / batch_size.


What is the simplest way to send data to TensorBoard?

Add summary ops to your TensorFlow graph, and write these summaries to a log directory. Then, start TensorBoard using

python tensorflow/tensorboard/ --logdir=path/to/log-directory

Every time I launch TensorBoard, I get a network security popup!

You can change TensorBoard to serve on localhost rather than '' by the flag --host=localhost. This should quiet any security warnings.

Extending TensorFlow

My data is in a custom format. How do I read it using TensorFlow?

There are three main options for dealing with data in a custom format.

The easiest option is to write parsing code in Python that transforms the data into a numpy array. Then, use to create an input pipeline from the in-memory data.

If your data doesn't fit in memory, try doing the parsing in the Dataset pipeline. Start with an appropriate file reader, like Then convert the dataset by mapping appropriate operations over it. Prefer predefined TensorFlow operations such as tf.decode_raw, tf.decode_csv, tf.parse_example, or tf.image.decode_png.

If your data is not easily parsable with the built-in TensorFlow operations, consider converting it, offline, to a format that is easily parsable, such as tf.python_io.TFRecordWriter format.


What is TensorFlow's coding style convention?

The TensorFlow Python API adheres to the PEP8 conventions.* In particular, we use CamelCase names for classes, and snake_case names for functions, methods, and properties. We also adhere to the Google Python style guide.

The TensorFlow C++ code base adheres to the Google C++ style guide.

(* With one exception: we use 2-space indentation instead of 4-space indentation.)

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